POS0094 INSIGHT INTO GIANT CELL ARTERITIS PATHOGENESIS BY NANOSTRING NCOUNTER GENE EXPRESSION PROFILING IN TEMPORAL ARTERY BIOPSIES
Authors: I. Ferrigno, M. Bonacini, A. Rossi, F. Muratore, L. Boiardi, A. Cavazza, A. Bisagni, L. Cimino, A. Ghidini, G. Malchiodi, A. Zerbini, C. Salvarani, S. Croci
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Giant Cell Arteritis (GCA) is a systemic inflammatory disease that affects large and medium-sized arteries. It is the most common vasculitis in people older than 50 years of age [1]. Temporal artery biopsies (TABs) are routinely performed for diagnosis, giving the invaluable opportunity to analyze inflamed tissues. Different histological patterns of inflammation can be found in TABs: transmural inflammation (TMI) is the most frequent pattern (78% of the positive TABs), while inflammation limited to adventitia (ILA) can be found in 7% of the positive TABs [2]. It is not known if ILA precedes TMI or if they are distinct GCA subsets. Knowledge on disease pathogenesis is growing [3], but high-throughput assays in TABs are still scarce [4-7]. GCA patients are actually treated with glucocorticoids, irrespective of clinical presentations and inflammation patterns. It is necessary to increase the knowledge on the molecular features of GCA to implement precision medicine. <h3>Objectives</h3> To identify differentially expressed genes (DEGs) in TAB from patients with GCA versus controls. To increase the knowledge on TMI and ILA histological patterns in GCA. <h3>Methods</h3> A retrospective cohort of patients subjected to TABs at AUSL-IRCCS at Reggio Emilia (Italy) was included: 42 patients had GCA with TMI; 7 patients had GCA with ILA; 7 patients had normal TABs and received a different diagnosis. All the patients with TMI and normal TABs were naïve from therapy while 4/7 patients with ILA were receiving glucocorticoids at the time of TABs. RNA was extracted from formalin-fixed paraffin-embedded TABs. The expression of 770 genes was profiled with the NanoString nCounter PanCancer Immune Profiling Panel. Data were analyzed with nSolver software 4.0. Background thresholding using the negative control probes was applied, then data were normalized over the positive control probes and 18 housekeeper genes. DEGs with fold changes &gt;2.0 and Benjamini-Yekutieli adjusted p-values &lt;0.05 were considered significant. Unsupervised clustering was performed with nSolver. Pathway analysis was performed with DAVID. <h3>Results</h3> Unsupervised clustering based on the expression of the 770 genes revealed two groups of samples: the first group contained all the normal TABs plus TABs with ILA, while the second group contained 41/42 TABs with TMI. In particular, TNF superfamily genes or genes related to senescence allowed to cluster TABs with TMI from ILA and normal TABs. TABs with TMI showed 31 down- and 268 up-regulated genes compared to normal TABs; 20 down- and 135 up-regulated genes compared to TABs with ILA. TABs with ILA revealed a gene expression profile similar to normal TABs: 47 genes reached fold changes &gt;2.0, but p-values did not maintain statistical significance after correction for multiple testing. These genes were also modulated in TABs with TMI. The chemokine CCL18 was the top up-regulated gene, while the cell adhesion molecule MCAM was the top down-regulated gene in TMI. CD86 costimulatory molecule, a marker of antigen-presenting cells, was detected only in TMI. JAK3 resulted increased in TMI, while JAK1 and JAK2 were not modulated. CD27 activating immune checkpoint was detected in all the TABs with TMI and 6/7 TABs with ILA, while it was not detected in normal TABs. The following pathways were enriched with DEGs: chemokine and TLR signaling, antigen processing and presentation via MHC class II, positive regulation of T cell activation, cellular responses to IL-1, TNF, IFNγ. <h3>Conclusion</h3> TABs with TMI had a distinct transcriptome compared to TABs with ILA. A set of genes were deregulated in TABs both with TMI and ILA versus normal TABs, indicating that ILA might represent the early stages of the disease. Gene profiling allowed to deepen the knowledge on GCA pathogenesis. <h3>References</h3> [1]Salvarani C. Nat Rev Rheumatol 2012 [2]Cavazza A. Am J of Surg Pathol 2014 [3]Ciccia F. Lancet Rheumatol 2021 [4]Andrew N. Neurol Neuroimmunol Neuroinflamm 2021 [5]Patrick C. Ann Rheum Dis 2016 [6]Friedman M. Arthritis Rheumatol 2022, ACR abstract 0486 [7]Croci S. Ann Rheum Dis 2016 <h3>Acknowledgements</h3> Azienda Unità Sanitaria Locale (AUSL)-IRCCS, Reggio Emilia, Italy: "Bando per la valorizzazione della Ricerca Istituzionale 2019" and Foundation for Research in Rheumatology (FOREUM). <h3>Disclosure of Interests</h3> None Declared.